Patrick Kahardipraja

According to our database, Patrick Kahardipraja authored at least 8 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2025

Attribution-guided Pruning for Compression, Circuit Discovery, and Targeted Correction in LLMs.

Sayed Mohammad Vakilzadeh Hatefi, Maximilian Dreyer, Reduan Achtibat, Patrick Kahardipraja, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin

CoRR, June, 2025

The Atlas of In-Context Learning: How Attention Heads Shape In-Context Retrieval Augmentation.

Patrick Kahardipraja, Reduan Achtibat, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin

CoRR, May, 2025

A Close Look at Decomposition-based XAI-Methods for Transformer Language Models.

Leila Arras, Bruno Puri, Patrick Kahardipraja, Sebastian Lapuschkin, Wojciech Samek

CoRR, February, 2025

FADE: Why Bad Descriptions Happen to Good Features.

Bruno Puri, Aakriti Jain, Elena Golimblevskaia, Patrick Kahardipraja, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin

Proceedings of the Findings of the Association for Computational Linguistics, 2025

2024

When Only Time Will Tell: Interpreting How Transformers Process Local Ambiguities Through the Lens of Restart-Incrementality.

Brielen Madureira, Patrick Kahardipraja, David Schlangen

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

2023

The Road to Quality is Paved with Good Revisions: A Detailed Evaluation Methodology for Revision Policies in Incremental Sequence Labelling.

Brielen Madureira, Patrick Kahardipraja, David Schlangen

Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue, 2023

TAPIR: Learning Adaptive Revision for Incremental Natural Language Understanding with a Two-Pass Model.

Patrick Kahardipraja, Brielen Madureira, David Schlangen

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2021

Towards Incremental Transformers: An Empirical Analysis of Transformer Models for Incremental NLU.

Patrick Kahardipraja, Brielen Madureira, David Schlangen

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021